Vehicle for performing minimal risk maneuver during autonomous driving and method of operating the vehicle

The present application claims under 35 U.S.C. § 119(a) the benefit of Korean Patent Application No. 10-2022-0113973, filed on Sep. 8, 2022 in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference.

The present disclosure relates to a vehicle for performing a minimum risk maneuver during autonomous driving, and a method for operating the vehicle.

Advanced driver assistance systems (ADAS) have been developed to assist drivers in driving a vehicle, e.g., under autonomous control. ADAS includes multiple sub-classifications and provides convenience to the driver. ADAS may also be referred to as autonomous driving or ADS (Automated Driving System).

Meanwhile, an abnormality may occur in an autonomous driving system while a vehicle performs autonomous driving. The vehicle may enter a dangerous state if appropriate measures are not taken to rectify the abnormality in the autonomous driving system.

Accordingly, various embodiments of the present disclosure disclose a vehicle that performs a minimal risk maneuver (MRM) to remove (or reduce) a risk and a method for operating the vehicle when a situation in which normal autonomous driving is impossible is detected during autonomous driving.

Various embodiments of the present disclosure provide a method for determining a minimum risk maneuver strategy based on vehicle state information or surrounding environment information, when a situation in which normal autonomous driving is impossible is detected during autonomous driving.

Various embodiments of the present disclosure provide a method and an apparatus for determining a minimum risk maneuver strategy considering a surrounding environment, when a situation in which normal autonomous driving is impossible is detected during autonomous driving.

One embodiment is an autonomous driving vehicle including: at least one sensor detecting surrounding environment of the vehicle and generating surrounding environment information; a processor monitoring state of the vehicle to generate vehicle state information and controlling autonomous driving of the vehicle; and a controller controlling operation of the vehicle according to control of the processor. Further, the processor may detect whether a minimum risk maneuver (MRM) is required or not based on at least one of the surrounding environment information or the vehicle state information during autonomous driving of the vehicle, determine an MRM type based on a possibility of colliding with a neighboring vehicle when the MRM is required, and control to stop the vehicle based on the determined MRM type.

According to the embodiment, when the MRM is required, the processor may determine MRM types possible to be performed based on the vehicle state information and the surrounding environment information, when a plurality of the MRM types are possible to be performed, the processor may determine the possibility of colliding with the neighboring vehicle with respect to a first MRM type having a highest priority among the plurality of the MRM types, when it is determined that there is no possibility of colliding with the neighboring vehicle, the processor may determine the first MRM type as an MRM type to be performed, and when it is determined that there is the possibility of colliding with the neighboring vehicle, the processor may determine another MRM type having a lower priority than the first MRM type as an MRM type to be performed.

According to the embodiment, the processor may calculate a longitudinal safe distance and a lateral safe distance to the neighboring vehicle, and determine a possibility of colliding with the neighboring vehicle based on the longitudinal safe distance and the lateral safe distance, and the longitudinal safe distance may be calculated as a difference between a minimum longitudinal relative distance to be maintained to the neighboring vehicle and an actual longitudinal relative distance to the neighboring vehicle, and the lateral safe distance may be calculated as a difference between a minimum lateral relative distance to be maintained to the neighboring vehicle and an actual lateral relative distance to the neighboring vehicle.

According to the embodiment, the neighboring vehicle may be a neighboring vehicle located in front of or on a front-lateral side of the vehicle.

According to the embodiment, the minimum longitudinal relative distance and the minimum lateral relative distance to be maintained to the neighboring vehicle located in front or on a front-lateral side of the vehicle may be set greater than the minimum longitudinal relative distance and the minimum lateral relative distance to be maintained to a neighboring vehicle located on a rear side or on a rear lateral side of the vehicle.

According to the embodiment, the autonomous driving vehicle may further include: a memory, and the processor may store the calculated longitudinal safe distance and lateral safe distance in the memory as base data for determination of the MRM to be performed.

According to the embodiment, the processor may determine the MRM type possible to be performed within a designated range based on the vehicle state information and the surrounding environment information.

According to the embodiment, the processor may determine whether lateral control is possible or not based on the vehicle state information, and determine a straight stop type as the MRM type possible to be performed when the lateral control is impossible.

According to the embodiment, when the lateral control is possible, the processor may determine the MRM type possible to be performed based on at least one of presence of a shoulder of a road within the designated range, a size of a shoulder of a road, or a possibility of lane detection.

According to the embodiment, the processor may determine whether the lane detection is possible or not, when there is no shoulder of a road within the designated range; determine a straight stop type as the MRM type possible to be performed, when the lane detection is impossible; and determine an in-lane stop and the straight stop type as the MRM types possible to be performed, when the lane detection is possible.

According to the embodiment, the processor may compare a size of the shoulder of a road with a size of the vehicle when there is the shoulder of a road within the designated range; determine a half-shoulder stop type, the in-lane stop type and the straight stop type as the MRM types possible to be performed when a size of the shoulder of a road is smaller than a size of the vehicle; and determine a full-shoulder stop type, the half-shoulder stop type, the in-lane stop type and the straight stop type as the MRM types possible to be performed when a size of the shoulder of a road is smaller than a size of the vehicle.

According to the embodiment, the autonomous driving vehicle may further include a memory, and the processor may store information of at least one of the possibility of the lateral control, the presence of the shoulder of a road, the size of the shoulder of a road, or the possibility of lane detection in the memory.

Another embodiment is a method for operating an autonomous driving vehicle including: obtaining surrounding environment information by detecting a surrounding environment of the vehicle during an autonomous driving of the vehicle; obtaining vehicle state information by monitoring a state of the vehicle during the autonomous driving of the vehicle; detecting whether a minimum risk maneuver (MRM) is required or not based on at least one of the surrounding environment information or the vehicle state information during the autonomous driving of the vehicle; determining an MRM type based on a possibility of colliding with a neighboring vehicle when the MRM is required; and controlling the vehicle to stop based on the determined MRM type.

According to the embodiment, the determining an MRM type may include: determining an MRM type possible to be performed based on the vehicle state information and the surrounding environment information; when a plurality of the MRM types are possible to be performed, determining the possibility of colliding with the neighboring vehicle with respect to a first MRM type having a highest priority among the plurality of the MRM types; when it is determined that there is no possibility of colliding with a neighboring vehicle, determining the first MRM type as an MRM type to be performed; and when it is determined that there is the possibility of colliding with the neighboring vehicle, determining another MRM type having a lower priority than the first MRM type as an MRM type to be performed.

According to the embodiment, the determining the possibility of colliding with the neighboring vehicle may include: calculating the longitudinal safe distance and the lateral safe distance to the neighboring vehicle; and determining the possibility of colliding with the neighboring vehicle based on the longitudinal safe distance and the lateral safe distance, and the longitudinal safe distance may be calculated as a difference between a minimum longitudinal relative distance to be maintained to the neighboring vehicle and an actual longitudinal relative distance to the neighboring vehicle, and the lateral safe distance may be calculated as a difference between a minimum lateral relative distance to be maintained to the neighboring vehicle and an actual lateral relative distance to the neighboring vehicle.

According to the embodiment, the method may further include: storing the calculated longitudinal safe distance and lateral safe distance in the memory as base data for determination of the MRM type to be performed.

According to the embodiment, the determining an MRM type possible to be performed based on the vehicle state information and the surrounding environment information may include: determining the MRM type possible to be performed within a designated range based on the vehicle state information and the surrounding environment information.

According to the embodiment, the determining the MRM type possible to be performed within the designated range may include: determining whether lateral control is possible or not based on the vehicle state information; and determining a straight stop type as the MRM type possible to be performed when the lateral control is impossible.

According to the embodiment, the method may further include, when the lateral control is possible, determining the MRM type possible to be performed based on at least one of presence of a shoulder of a road within the designated range, a size of a shoulder of a road, or a possibility of lane detection.

According to the embodiment, the determining the MRM type possible to be performed based on at least one of presence of a shoulder of a road within the designated range, a size of a shoulder of a road, or a possibility of lane detection may include: determining whether the lane detection is possible or not, when there is no shoulder of a road within the designated range; determining a straight stop type as the MRM type possible to be performed, when the lane detection is impossible; and determining an in-lane stop and the straight stop type as the MRM types possible to be performed, when the lane detection is possible.

According to the embodiment, the method may further include: comparing a size of the shoulder of a road with a size of the vehicle when there is the shoulder of a road within the designated range; determining a half-shoulder stop type, the in-lane stop type and the straight stop type as the MRM types possible to be performed when a size of the shoulder of a road is smaller than a size of the vehicle; and determining a full-shoulder stop type, the half-shoulder stop type, the in-lane stop type and the straight stop type as the MRM types possible to be performed when a size of the shoulder of a road is smaller than a size of the vehicle.

According to the embodiment, the method may further include: storing information of at least one of the possibility of the lateral control, the presence of the shoulder of a road, the size of the shoulder of a road, or the possibility of lane detection in the memory.

According to various embodiments of the present disclosure, when a vehicle detects a situation in which normal autonomous driving is impossible during autonomous driving, the vehicle may improve safety by determining a minimum risk maneuver strategy based on vehicle state information, and/or surrounding environment information.

It is understood that the term “vehicle” or “vehicular” or other similar term as used herein is inclusive of motor vehicles in general such as passenger automobiles including sports utility vehicles (SUV), buses, trucks, various commercial vehicles, watercraft including a variety of boats and ships, aircraft, and the like, and includes hybrid vehicles, electric vehicles, plug-in hybrid electric vehicles, hydrogen-powered vehicles and other alternative fuel vehicles (e.g. fuels derived from resources other than petroleum). As referred to herein, a hybrid vehicle is a vehicle that has two or more sources of power, for example both gasoline-powered and electric-powered vehicles.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Throughout the specification, unless explicitly described to the contrary, the word “comprise” and variations such as “comprises” or “comprising” will be understood to imply the inclusion of stated elements but not the exclusion of any other elements. In addition, the terms “unit”, “-er”, “-or”, and “module” described in the specification mean units for processing at least one function and operation, and can be implemented by hardware components or software components and combinations thereof.

Further, the control logic of the present disclosure may be embodied as non-transitory computer readable media on a computer readable medium containing executable program instructions executed by a processor, controller or the like. Examples of computer readable media include, but are not limited to, ROM, RAM, compact disc (CD)-ROMs, magnetic tapes, floppy disks, flash drives, smart cards and optical data storage devices. The computer readable medium can also be distributed in network coupled computer systems so that the computer readable media is stored and executed in a distributed fashion, e.g., by a telematics server or a Controller Area Network (CAN).

In certain aspects, a present vehicle may be an autonomous vehicle.

In a fully autonomous vehicle or system, the vehicle may perform all driving tasks under all conditions and little or no driving assistance is required a human driver. In semi-autonomous vehicle, for example, the automated driving system may perform some or all parts of the driving task in some conditions, but a human driver regains control under some conditions, or in other semi-autonomous systems, the vehicle's automated system may oversee steering and accelerating and braking in some conditions, although the human driver is required to continue paying attention to the driving environment throughout the journey, while also performing the remainder of the necessary tasks.

In certain embodiments, the present systems and vehicles may be fully autonomous. In other certain embodiments, the present systems and vehicles may be semi-autonomous.

Hereinafter, embodiments of the present disclosure will be described in further detail with reference to the accompanying drawings.

The configuration and advantages of the present disclosure will be more apparent from the following detailed description. Wherever possible, the same reference numerals will be used throughout the drawings to refer to the same elements even if shown in another drawing. It will be paid attention that detailed description of known arts will be omitted if it is determined that the arts can mislead the present disclosure.

Before describing the present disclosure in detail, terms being used in the present disclosure may be defined as below.

The vehicle is equipped with an Automated Driving System (ADS) and is a vehicle capable of autonomous driving. For example, by the ADS, the vehicle may perform at least one of steering, acceleration, deceleration, lane change, or stopping (or short stop) without a driver's manipulation. For example, the ADS may include at least one of Pedestrian Detection and Collision Mitigation System (PDCMS), Lane Change Decision Aid System (LCDAS), Land Departure Warning System (LDWS), Adaptive Cruise Control (ACC), Lane Keeping Assistance System (LKAS), Road Boundary Departure Prevention System (RBDPS), Curve Speed Warning System (CSWS), Forward Vehicle Collision Warning System (FVCWS), or Low Speed Following (LSF).

A driver is a human being who uses a vehicle, and is a human being provided with a service of an autonomous driving system.

A vehicle control authority is an authority for controlling at least one component of the vehicle and/or at least one function of the vehicle. At least one function of the vehicle may include, for example, at least one of steering, acceleration, deceleration (or braking), lane change, lane detection, lateral control, obstacle recognition and distance detection, powertrain control, safe area detection, engine on/off, power on/off, or vehicle lock/unlock. The listed functions of the vehicle are merely examples for helping understanding, and embodiments of the present disclosure are not limited thereto.

A shoulder may mean a space between an outermost road boundary (or a boundary of an outermost lane) in a direction in which a vehicle is traveling and a road edge (e.g., curb, guardrail).

is a block diagram of a vehicle according to various embodiments of the present disclosure. The configuration of the vehicle illustrated inis one embodiment, and each component may be configured as one chip, one component, or one electronic circuit, or a combination of chips, components and/or electronic circuits. According to the embodiment, some of the components illustrated inmay be divided into a plurality of components and configured as different chips, different components, or different electronic circuits, and some components may be combined to form one chip, one component, or one electronic circuit. According to the embodiment, some of the components illustrated inmay be omitted or other components not illustrated may be added. At least some of the components ofwill be described with reference to.is a functional block diagram of the processor according to various embodiments of the present disclosure, andis a view illustrating minimum risk maneuver (MRM) strategies for each vehicle state according to various embodiments of the present disclosure.are example illustrations of determining an MRM strategy based on surrounding environment information within a designated MRC range of the vehicle according to various embodiments of the present disclosure, andis an example illustration in which a priority of the MRM strategy is changed according to surrounding object information within a designated MRC range of the vehicle according to various embodiments of the present disclosure.are example illustrations of determining whether a vehicle collides with a neighboring vehicle due to an MRM of the vehicle according to various embodiments of the present disclosure, andis an example illustration of determining an MRM strategy of a vehicle considering whether the vehicle collides with a neighboring vehicle due to a determined MRM of the vehicle according to various embodiments of the present disclosure.are example illustrations for calculating a distance from a vehicle according to various embodiments of the present disclosure to a neighboring vehicle.

Referring to, the vehiclemay include a sensor, a controller, a processor, a display, a communication apparatus, and a storage device.

According to various embodiments, the sensormay sense an environment around the vehicleand generate data related to the surrounding environment of the vehicle. According to the embodiment, the sensormay obtain road information, information on objects around the vehicle (e.g., other vehicles, people, objects, curbs, guardrails, lanes, obstacles) and/or location information of the vehicle based on the sensing data obtained from at least one sensor. The road information may include, for example, at least one of lane location, a shape of a lane, a color of a lane, a type of lane, the number of lanes, whether a shoulder exists, or a size of a shoulder. The object around the vehicle may include, for example, at least one of a position of the object, a size of the object, a shape of the object, a distance to the object, or a relative speed to the object.

According to the embodiment, the sensormay include at least one selected of a camera, a light detection and ranging (LIDAR) sensor, a radio detection and ranging (RADAR) sensor, an ultrasonic sensor, an infrared sensor, or a location measurement sensor. The listed sensors are merely examples for helping understanding, and the sensors included in the sensorof the present disclosure are not limited thereto. The camera may photograph the surroundings of the vehicleto generate image data that includes a lane and/or neighboring object positioned in front, on a rear side, and/or on a side of the vehicle. The LIDAR may generate information on an object located in front, on a rear side, and/or on a side of the vehicleusing light (or a laser). The radar may generate information on an object located in front, on a rear side and/or on a side of the vehicleusing electromagnetic waves (or radio waves). The ultrasonic sensor may generate information on an object located in front, on a rear side and/or on a side of the vehicleusing ultrasonic waves. The infrared sensor may generate information on an object located in front, on a rear side and/or on a side of the vehicleusing infrared rays. The location measurement sensor may measure the current location of the vehicle. The location measurement sensor may include at least one of a Global Positioning System (GPS) sensor, a Differential Global Positioning System (DGPS) sensor or a Global Navigation Satellite System (GNSS) sensor. The location measurement sensor may generate location data of the vehicle based on a signal generated by at least one of a GPS sensor, DGPS sensor or GNSS sensor.

According to various embodiments, the controllermay control operation of at least one component of the vehicleand/or at least one function of the vehicle according to the control of the processor. The at least one function may be, for example, at least one of a steering function, an acceleration function (or a longitudinal acceleration function), a deceleration function (or a longitudinal deceleration function, a brake function), a lane change function, a lane detection function, an obstacle recognition and a distance detection function, a lateral control function, a powertrain control function, a safety zone detection function, an engine on/off, a power on/off, or a vehicle lock/unlock function.

According to the embodiment, the controllermay control at least one component of the vehicle and/or at least one function of the vehicle for autonomous driving and/or a minimal risk maneuver (MRM) of the vehicleaccording to the control of the processor. For example, for the MRM, the controllermay control the operation of at least one function of a steering function, an acceleration function, a deceleration function, a lane change function, a lane detection function, a lateral control function, an obstacle recognition and distance detection function, a powertrain control function, or a safe zone detection function.

According to various embodiments, the processormay control the overall operation of the vehicle. According to the embodiment, the processormay include an electrical control unit (ECU) capable of integrally controlling components in the vehicle. For example, the processormay include a central processing unit (CPU) or micro processing unit (MCU) capable of performing arithmetic processing.